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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Obtaining Real Production Data Through On-Component Printed SIR Patterns

Cleanliness validation of printed circuit assemblies has become increasingly important as electronic assemblies become smaller, denser, and more challenging to clean, prompting an increase in reliance in custom solutions. With increasingly fast paced development cycles, long lead times and costs of quality custom surrogate test boards become prohibitive in obtaining cleanliness data that is a true representative of the final product. In this study, aerosol jet printing, an additive manufacturing technology for electronics, was used to manufacture surface insulation resistance (SIR) test structures that were evaluated on their ability to detect cleanliness defects and remain stable when clean, as well as their survivability to standard electronics wash processes. Additionally, in an effort to further increase the agility of true product testing, SIR test structures were printed directly onto ball-grid-arrays (BGAs) and tested the survivability of conformally printed leads for data acquisition. SIR test structures displayed good wash survivability on standard FR4 and BGAs, including wraparounds for on-component prints, and high sensitivity to cleanliness defects. The presented results demonstrate the feasibility of printed SIR structures as a quick-turn, highly customizable solution for wash validation with potential for increased fidelity cleanliness testing.

SIR, Additive Manufacturing, Printed Electronics, ↗
DOE OSTI2021

Structure and energetics of the elbows in the Au(111) herringbone reconstruction

We study the structure of the threading edge dislocations, or “elbows,” which are an essential component of the well-known herringbone reconstruction of the (111) surface of Au. Previous work had shown that these dislocations can be stabilized by long-range elastic relaxations into the bulk. However, the validity of the harmonic spring model that had been used to estimate the energies of the dislocations is uncertain. To enable a more refined model of the dislocation energetics, we have imaged the atomic structure of these dislocations using scanning tunneling microscopy. We find that the harmonic spring model does not adequately reproduce the observed structure. We are able to reproduce the structure, however, with a two-dimensional Frenkel-Kontorova (FK) model that uses a pairwise Morse potential to describe the interactions between the top layer Au atoms on a rigid substrate. The parameters of the potential were obtained by fitting the energy of uniaxially compressed phases, or “stripes”, computed with density functional theory, as a function of surface Au density. Within this model, the formation of the threading dislocations remains unfavorable. However, the large forces on the substrate atoms near the threading-dislocation cores, render the assumption of a completely rigid substrate questionable. Indeed, if the FK parameters are modified to account for the relaxation of just one more atomic layer, threading dislocations can, in principle, become favorable, even without bulk elastic relaxations. Additional evidence for a small elbow energy is that our computed change in the Au(111) surface stress tensor caused by the (√ 3 × 22) reconstruction is considerably smaller than previous estimates.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Development of Digital Twin Predictive Model for PWR Components: Updates on Multi Times Series Temperature Prediction Using Recurrent Neural Network, DMW Fatigue Tests, System Level Thermal-Mechanical-Stress Analysis

The long-term operation (LTO) of nuclear power plant (NPP) beyond their original design life of 40 years, can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Frequent NDE requirement may lead to frequent shutdown of the NPP. This in turn can lead to power outage and additional NDE-inspection-cost related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing more digital approach such as by using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. Towards this goal Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program is working on the development of a DT framework that can be used for real time environmental fatigue prediction of reactor components. The DT framework is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics such as finite element (FE) based modeling tools. Towards this overall goal, following are some of the major contributions made during the FY21: 1) Multiple 82/182 dissimilar metal weld (DMW) specimens (both solid-weld and joint-weld representing the actual reactor multi-metal nozzles) were fatigue tested. The resulting fatigue lives were compared to the NUREG-6909 based best-fit and design fatigue curves. Additionally, the results of 52/152 DMW fatigue specimens (which were recently tested at Republic of Korea under the sponsorship of International Nuclear Energy Research Initiative - INERI program) were compared to the NUREG-6909 based best-fit and design fatigue curves. From the comparison of 82/182 and 52/152 DMW test data with NUREG-6909 best-fit curve, most of the reported test data fall way away from the NUREG-6909 suggested best-fit or mean curve. The NUREG-6909 suggested best-fit curve is the best-fit curve of austenitic stainless steel and due to lack of enough data on Nickel-based welds, this is currently being used for predicting the life of Nickel-alloy-based welded components. However, the above observation may require higher scaling factor (e.g., ASME suggested factor of 20 on cycles rather than the current NUREG-6909 suggested factor of 12 on cycles) for scaling the austenitic-stainless-steel best-fit-curve for estimating the design or safe-life of a welded component. Accordingly, for example, if a DMW component experience a strain amplitude of 0.6% the PWR-water life of the component would be 52 cycles instead of 85 cycles. However, more DMW tests are required to further ascertain the above-mentioned observations. 2) A system level CAD and finite element model were developed which consists of reactor pressure vessel (RPV), part of steam generator (SG), part of pressurizer (PRZ), hot leg (HL), and surge line (SL). This is with detailed nozzle geometry and thermal-mechanical material properties of different metals to simulate realistic thermal-mechanical stress under connected system global thermal-mechanical boundary conditions. 3) Different system level heat transfer analyses were performed with estimation of relevant heat transfer coefficients. The resulting data were used in subsequent system level thermal-mechanical stress analysis and for generating spatial-temporal training and validation data for a system level digital-twin based temperature predictor. Transient heat transfer analyses were performed considering thermal boundary condition under design-basis (DB) loading and EDF (Électricité de France) data-based grid-load-following (EDF-GLF) loading cycles. 4) System level thermal-mechanical stress analysis was performed for identifying damage-prone hotspots and for future extension of the model for cyclic state prediction. From the system-level model simulation under DB loading cycle it is found that HL and the SL nozzle that connect to the HL can experience significant stress and strain and could be one of the weakest links in the overall reactor coolant system (RCS). 5) An AI/ML based DT model was developed for multi-time-series temperature prediction at any inside/outside thickness locations of PWR pressure boundary components. This is by using Recurrent-neural-network (RNN) and keras machine learning libraries. The RNN model was validated against two laboratory test-based data sets with one obtained through ANL’s in-air fatigue test system and other through PWR-water test loop. The experimentally validated DT model further validated against FE model results to predict thermal scarification related spatialtemporal temperatures at random locations of a component. The well validated DT model was then used for demonstrating spatial-temporal temperature prediction under 100+ years of reactor operation subjected to combined DB, EDF-GLF and randomized grid-load-following (RANDOMGLF) loading Cycles. The expert-elicitation DT model framework was developed assuming field/input/process measurements can be available from a few existing plant sensors and can readily be used by the NPP operators. The above temperature prediction model will feed to the next-step stress analysis model based on which the life of a component can be predicted in realtime, which is one of our future works.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Hybrid AI-ML and FE-based Digital Twin Predictive Modeling Framework for a PWR Coolant System Components: Updates on Multi-Time-Series-3D-Location Dependent Usages Factor Prediction

The long-term operation (LTO) of nuclear power plants (NPP) beyond their original design life of 40 years can lead to more material damage associated with cyclic fatigue under thermal-mechanical loading cycles and associated long-term exposure of reactor material to the deleterious reactor-coolant environments. However, under this LTO condition, the reactor components can still safely operate but may require more frequent Nondestructive Evaluation (NDE) of reactor components. Requiring frequent NDE inspections may lead to frequent NPP shutdowns which can lead to power outages and additional NDE inspection cost-related economic loss. The economic loss can be minimized by reducing uncertainty in life estimation of safety-critical pressure boundary components and by implementing a more digital approach such as using upcoming digital-twin (DT) technology for predicting the structural states (e.g., time and location dependent inside/outside thickness temperature, stress, strain, plastic deformation, etc.) and associated fatigue life of a component in real time. The DT framework is based on limited experimental data, Artificial-intelligence (AI)-Machine-Learning (ML) and multiphysics-computationalmechanics such as finite element- (FE) based models. Given the real-time thermal-hydraulic process measurements from several existing plant sensors, the overall goal of the DT framework is to predict the cumulative usages factors or equivalent fatigue lives in real time and at any random 3D location of the components. This includes inaccessible locations such as inside the thickness or location of a component. This prediction can be at thousands to millions of 3D point clouds or locations like conventional FE-based models, but without running an FE model in real time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Mechanical Characterization of Electrolyzer Membranes and Components Under Compression

Proton-exchange membrane (PEM) water electrolysis is a promising technology for producing clean hydrogen by electrochemically splitting water when paired with renewable energy sources. A major roadblock to improving electrolyzer durability is the mechanical degradation of the cell components, which requires an understanding of their mechanical response under device-relevant conditions. However, there is a lack of studies on the mechanical characterization of the PEM and other components, as well as and their interactions. This study aims to address this gap by using a custom-designed testing apparatus to investigate the mechanics of electrolyzer components in uniaxial compression at 25 and 80 °C. Findings show stress-strain response of components have a varying degree of nonlinearity owing to their distinct deformation mechanisms and morphologies, from porous structures to polymers. These results are used to develop an expression for compressive stress-strain response of Nafion membranes and then analyze the deformation of components under applied pressure by using a 1-D spring network model of cell assembly. This work provides a new understanding of mechanical responses of the electrolyzer membrane and cell components, which can help assess material design and cell assembly strategies for improved electrolyzer durability.

08 HYDROGEN↗
DOE OSTI2021

Importance of dynamic lattice effects for crystal field excitations in the quantum spin ice candidate Pr 2 Zr 2 O 7

We explore dynamic interactions between the crystal lattice and magnetic degrees of freedom in a frustrated magnetic system using the example of a pyrochlore quantum spin-ice candidate Pr 2 Zr 2 O 7 . Using Raman scattering spectroscopy we demonstrate that crystal electric field excitations of Pr3+, which define the magnetic properties of Pr 2 Zr 2 O 7 , cannot be understood within a model of a static lattice. We identify vibronic interactions with a phonon which lead to a splitting of a doublet crystal field excitation at around 55 meV. We also observe an unconventional behavior of a splitting of the non-Kramers ground state doublet of Pr 3+ , revealed by observing excitations to the first excited singlet state $E$$^{0}_{g}$ → A 1 g at around 10 meV. The splitting has a strong temperature dependence, where the doublet structure is most prominent between 50 and 100 K, and the weight of one of the components strongly decreases on cooling contrary to simple thermal population tendency. Furthermore, we suggest a static or dynamic deviation of Pr 3+ from the position in the ideal crystal structure can be the origin of the effect, with the deviation strongly decreasing at low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Structural Studies of Alloyed and Nanoparticle Transition Metal Dichalcogenides by Selenium-77 Solid-State Nuclear Magnetic Resonance Spectroscopy

Layered transition metal dichalcogenides (TMDCs) such as MoS 2 , MoSe 2 , and WSe 2 are under intense investigation because they are atomically thin semiconductors with photophysical properties that can be tuned by changing their composition or morphology. Mechanochemical processing has been proposed as a method to obtain alloyed TMDCs in the series Mo 1–x W x S y Se 2–y (x = 0–1; y = 0–2). However, elucidating the chemical transformations occurring at the atomic scale following mechanochemical processing can be challenging because the products are often amorphous or microcrystalline. To address this challenge, we probe TMDC mixing and alloying by using a combination of powder X-ray diffraction, Raman spectroscopy, diffuse reflectance spectroscopy, 77 Se solid-state nuclear magnetic resonance (SSNMR) spectroscopy, and planewave density functional theory (DFT) calculations. The nature of the milling material and reaction atmosphere are shown to be essential factors in limiting the formation of undesired oxide byproducts. We demonstrate acquisition of 77 Se SSNMR spectra using different combinations of Carr-Purcell Meiboom-Gill acquisition (CPMG) pulse sequences, magic angle spinning (MAS), and MAS dynamic nuclear polarization. Further, the combination of SSNMR with the other characterization methods clearly demonstrates that high energy impact ball milling induces molecular level alloying of Mo, W and chalcogen atoms in the family Mo 1–x W x S y Se 2–y . Gauge including projector augmented wave DFT calculations yield accurate 77 Se chemical shift (CS) tensor components. 77 Se SSNMR spectroscopy was also applied to study the structure of WSe 2 nanocrystals intercalated with ethylenediamine. The intercalated WSe 2 nanocrystals exhibit a more positive isotropic 77 Se CS as compared to bulk WSe 2 , however, the 77 Se CS anisotropy is the same, confirming the WSe 2 layers have a similar structure as in their bulk counterparts.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Structure of VanS from vancomycin-resistant enterococci: A sensor kinase with weak ATP binding

The VanRS two-component system regulates the resistance phenotype of vancomycin-resistant enterococci. VanS is a sensor histidine kinase that responds to the presence of vancomycin by autophosphorylating and subsequently transferring the phosphoryl group to the response regulator, VanR. The phosphotransfer activates VanR as a transcription factor, which initiates the expression of resistance genes. Structural information about VanS proteins has remained elusive, hindering the molecular-level understanding of their function. Here, we present X-ray crystal structures for the catalytic and ATP-binding (CA) domains of two VanS proteins, derived from vancomycin-resistant enterococci types A and C. Both proteins adopt the canonical Bergerat fold that has been observed for CA domains of other prokaryotic histidine kinases. We attempted to determine structures for the nucleotide-bound forms of both proteins; however, despite repeated efforts, these forms could not be crystallized, prompting us to measure the proteins' binding affinities for ATP. Unexpectedly, both CA domains displayed low affinities for the nucleotide, with K D values in the low millimolar range. Since these K D values are comparable to intracellular ATP concentrations, this weak substrate binding could reflect a way of regulating expression of the resistance phenotype.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Productive Programming of Distributed Systems with the SHAD C++ Library

High-performance computing (HPC) is often perceived as a matter of making large-scale systems (e.g., clusters) run as fast as possible, regardless the required programming effort. However, the idea of "bringing HPC to the masses" has recently emerged. Inspired by this vision, we have designed SHAD, the Scalable High-performance Algorithms and Data-structures library. SHAD is open source software, written in C++, for C++ developers. Unlike other HPC libraries for distributed systems, which rely on SPMD models, SHAD adopts a shared-memory programming abstraction, to make C++ programmers feel at home. Underneath, SHAD manages tasking and data-movements, moving the computation where data resides and taking advantage of asynchrony to tolerate network latency. At the bottom of his stack, SHAD can interface with multiple runtime systems: this not only improves developer’s productivity, by hiding the complexity of such software and of the underlying hardware, but also greatly enhance code portability. Thanks to its abstraction layers, SHAD can indeed target different systems, ranging from laptops to HPC clusters, without any need for modifying the user-level code. We have prototyped and open-sourced the implementation of (a subset of) the C++ standard library (STL) targeting multi-node HPC clusters. Our work allows plain STL-based C++ code to scale on HPC systems, with no need for rewriting the code to exploit the complex hardware. SHAD is available under Apache v2 License at https://github.com/pnnl/SHAD. In this paper we overview the design of the SHAD library, depicting its main components: runtime systems abstractions for tasking; parallel and distributed data-structures; STL-compliant interfaces and algorithms.

Castellana, Vito G.↗
DOE OSTI2021

Orbital Angular Momentum from Self‐Assembled Concentric Nanoparticle Rings

Abstract Ring‐shaped nanostructures can focus, filter, and manipulate electromagnetic waves, but are challenging to incorporate into devices using standard nanofabrication techniques. Directed self‐assembly (DSA) of block copolymers (BCPs) on lithographically patterned templates has successfully been used to fabricate concentric rings and spirals as etching masks. However, this method is limited by BCP phase behavior and material selection. Here, a straightforward approach to generate ring‐shaped nanoparticle assemblies in thin films of supramolecular nanocomposites is demonstrated. DSA is used to guide the formation of concentric rings with radii spanning 150–1150 nm and ring widths spanning 30–60 nm. When plasmonic nanoparticles are used, ring nanodevice arrays can be fabricated in one step, and the completed devices produce high‐quality orbital angular momentum (OAM). Nanocomposite DSA simplifies and streamlines nanofabrication by producing metal structures without etching or deposition steps; it also introduces interparticle coupling as a new design axis. Detailed analysis of the nanoparticle ring assemblies confirms that the supramolecular system self‐regulates the spatial distribution of its components, and thus exhibits a degree of flexibility absent in DSA of BCPs alone, where structures are determined by polymer‐pattern incommensurability. The present studies also provide guidelines for developing self‐regulating DSA as an alternative to incommensurability‐driven methods.

Vargo, Emma↗
DOE OSTI2020

Structure, properties, and applications of polyacrylonitrile/carbon nanotube ( CNT ) fibers at low CNT loading

Abstract Bi‐component, polyacrylonitrile (PAN)/carbon nanotube (CNT) fibers were processed, at different core‐sheath area ratios, by gel spinning. A percolated CNT network at 10 wt% CNT in the sheath enhanced electrical conductivity as compared to the neat PAN fiber, while PAN polymer in the core contributed to the good mechanical properties. Fibers with relatively thin sheath allowed overall CNT loading as low as 3.7 wt% to be made with good electrical conductivity, and PAN stabilization by Joule heating was demonstrated. Such fibers with combined good mechanical properties and electrical conductivity can also potentially be used for electrical heating of fabrics, for making smart textiles, and for electromagnetic interference shielding.

Lu, Mingxuan↗
DOE OSTI2020

Application of entropy and signal energy for ultrasound-based classification of three-dimensional printed polyetherketoneketone components

This paper describes a preliminary method for the classification of annealed and unannealed polyetherketoneketone (PEKK) components manufactured using a material extrusion three-dimensional (3D) printing process. PEKK is representative of a class of high-performance thermoplastics that are increasingly employed as feedstocks for use in 3D printing. PEKK components may be used continuously at elevated temperatures, are chemically resistant, and able to withstand large mechanical loads. These properties render PEKK suitable as a metal component replacement in aerospace applications, high-temperature industrial applications, and surgical implants. The structure of PEKK is semi-crystalline with the specific crystallinity correlating to the final properties during application, making determination of this property crucial. This study compares three different signal processing techniques intended to distinguish annealed (high crystallinity) from unannealed (low crystallinity) components using backscattered ultrasound. The first is energy-based and is unable to detect annealing. The second two are based on different entropies of the backscattered signal: a limiting form of Renyi's entropy and a limiting form of joint entropy. The joint entropy values for the annealed and unannealed specimens fall into two non-overlapping intervals and have a statistical separation of two standard deviations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Multi-Scale Modeling of the Evolution of Structure and Properties in Materials for Nuclear Energy Applications [Slides]

Nuclear energy is an important component of an overall strategy to address climate change. Idaho National Laboratory (INL) is the U.S. Department of Energy’s primary facility for research and development in nuclear science and technology for energy generation, supporting the improvement and life extension of the existing reactor fleet and the development and licensing of new reactor designs. Computational modeling is an important component of these activities, particularly in the area of materials for nuclear applications, where experimental data can be very challenging and expensive to acquire, and where data is especially scarce for new reactor designs. INL has used multi-scale modeling – linking atomistic, mesoscale, and engineering scales – to improve the ability to predict the performance of materials for nuclear energy applications. In this talk, I will give an overview of the approach and tools used, and several examples of application, including performance of nuclear fuels, understanding radiation-driven formation of nanoscale void and gas bubble superlattices, and powder densification through electric field assisted sintering.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Developing a new ethylene glycol/H 2 O pretreatment system to achieve efficient enzymatic hydrolysis of sugarcane bagasse cellulose and recover highly active lignin: Countercurrent extraction

Improving pretreatment efficiency is a critical premise in achieving efficient biomass conversion, and obtaining high-performance natural polymers is the guarantee of high-value conversion of biomass. Here, in this study, a new pilot-scale continuous countercurrent pretreatment reaction unit about ethylene glycol-alkali solution was designed for pretreating sugarcane bagasse in order to achieve efficient separation of the three major components of lignocellulose when expanding the scale of pretreatment, reduce lignin deposition on the fiber surface, and obtain highly active lignin and excellent enzymatic hydrolysis efficiency of cellulose. X-ray diffractometer (XRD), X-ray photoelectron spectrometer (XPS), brunauer-emmett-teller (BET) and scanning electron microscope (SEM) methods are used to analyze the structural properties of sugarcane bagasse before and after pretreatment, and high-performance liquid chromatography (HPLC) is used to analyze the monosaccharide components in the enzymatic solution. In addition, the structural properties of the recovered lignin are analyzed by gel permeation chromatography (GPC), 31 P NMR and 2D-HSQC-NMR methods. The results indicate that the system can gain a high cellulose recovery of 92.99% along with a lignin removal of 95.33%, and recovered lignin has low lignin carbohydrate complexes, low condensation, and rich in phenolic hydroxyl groups for 1.95 mmol/g. Meanwhile, the countercurrent pretreatment system can effectively reduce the deposition of lignin on the cellulose surface, which is evidently superior to the non-countercurrent pretreatment and facilitates the efficiency of enzymatic saccharification of substrate, achieving a high glucose yield of 99% as well as a total sugar yield of 91.11%. The method efficiently separates biomass in a green manner, and solid residues are easily hydrolyzed, showing potential for industrial-scale production.

09 BIOMASS FUELS↗
DOE OSTI2025

Structure Prediction of Ionic Epitaxial Interfaces with Ogre Demonstrated for Colloidal Heterostructures of Lead Halide Perovskites

Colloidal epitaxial heterostructures are nanoparticles composed of two different materials connected at an interface, which can exhibit properties different from those of their individual components. Combining dissimilar materials offers exciting opportunities to create a wide variety of functional heterostructures. However, assessing structural compatibility–the main prerequisite for epitaxial growth–is challenging when pairing complex materials with different lattice parameters and crystal structures. This complicates both the selection of target heterostructures for synthesis and the assignment of interface models when new heterostructures are obtained. Here, we demonstrate Ogre as a powerful tool to accelerate the design and characterization of colloidal heterostructures. To this end, we implemented developments tailored for the high-efficiency prediction of epitaxial interfaces between ionic/polar materials, which encompass most colloidal semiconductors. These include the use of pre-screening candidate models based on charge balance at the interface and the use of a classical potential for fast energy evaluations, with parameters automatically calculated based on the input bulk structures. These developments are validated for perovskite-based CsPbBr 3 /Pb 4 S 3 Br 2 heterostructures, where Ogre produces interface models in excellent agreement with density functional theory and experiments. Furthermore, we use Ogre to rationalize the templating effect of CsPbCl 3 on the growth of lead sulfochlorides, where perovskite seeds induce the formation of Pb 4 S 3 Cl 2 rather than Pb 3 S 2 Cl 2 due to better epitaxial compatibility. Finally, combining Ogre simulations with experimental data enables us to unravel the structure and composition of the hitherto unsolved CsPbBr 3 /Bi x Pb y S z interface, and to assign a structure to several other reported metal halide- and oxide-based interfaces. The Ogre package is available on GitHub or via the OgreInterface desktop application, available for Windows, Linux, and Mac.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗